论文标题
心脏：心脏信号的分层深层生成模型
CardiacGen: A Hierarchical Deep Generative Model for Cardiac Signals
论文作者
论文摘要
我们提出心脏，这是一个深度学习框架，用于产生合成但生理上合理的心脏信号（例如ECG）。基于心血管系统功能的生理学，我们提出了一个模块化层次生成模型，并使用多目标损耗函数对训练每个模块施加明确的正规约束。该模型包括2个模块，这是一个重点是产生现实的心率变化特征的HRV模块和一个专注于为不同方式生成现实信号形态的形态模块。我们从经验上表明，除了具有现实的生理特征外，来自心脏的合成数据可用于数据增强，以提高基于深度学习的分类器的性能。心脏代码可从https://github.com/sense-lab-osu/cardiac_gen_model获得。
We present CardiacGen, a Deep Learning framework for generating synthetic but physiologically plausible cardiac signals like ECG. Based on the physiology of cardiovascular system function, we propose a modular hierarchical generative model and impose explicit regularizing constraints for training each module using multi-objective loss functions. The model comprises 2 modules, an HRV module focused on producing realistic Heart-Rate-Variability characteristics and a Morphology module focused on generating realistic signal morphologies for different modalities. We empirically show that in addition to having realistic physiological features, the synthetic data from CardiacGen can be used for data augmentation to improve the performance of Deep Learning based classifiers. CardiacGen code is available at https://github.com/SENSE-Lab-OSU/cardiac_gen_model.
